作者机构：
[Chen Mao; Peng Xicheng; Zhang Jingzhong] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Zhang Jingzhong] Guangzhou Univ, Inst Computat Sci & Technol, Guangzhou 510006, Guangdong, Peoples R China.;[Zhang Jingzhong] Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 610041, Peoples R China.
通讯机构：
[Chen Mao] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
关键词：
Geometry algebra;point geometry;proof method based on identical equations;vector geometry;Wu's method
摘要：
The algebraic methods represented by Wu's method have made significant breakthroughs in the field of geometric theorem proving. Algebraic proofs usually involve large amounts of calculations, thus making it difficult to understand intuitively. However, if the authors look at Wu's method from the perspective of identity,Wu's method can be understood easily and can be used to generate new geometric propositions. To make geometric reasoning simpler, more expressive, and richer in geometric meaning, the authors establish a geometric algebraic system (point geometry built on nearly 20 basic properties/formulas about operations on points) while maintaining the advantages of the coordinate method, vector method, and particle geometry method and avoiding their disadvantages. Geometric relations in the propositions and conclusions of a geometric problem are expressed as identical equations of vector polynomials according to point geometry. Thereafter, a proof method that maintains the essence of Wu's method is introduced to find the relationships between these equations. A test on more than 400 geometry statements shows that the proposed proof method, which is based on identical equations of vector polynomials, is simple and effective. Furthermore, when solving the original problem, this proof method can also help the authors recognize the relationship between the propositions of the problem and help the authors generate new geometric propositions.
作者：
Liu, Sannyuya;Peng, Xian*;Cheng, Hercy N. H.;Liu, Zhi;Sun, Jianwen（孙建文）;...
期刊：
Journal of Educational Computing Research,2019年57(3):670-696 ISSN：0735-6331
通讯作者：
Peng, Xian
作者机构：
[Sun, Jianwen; Liu, Sannyuya; Cheng, Hercy N. H.; Liu, Zhi; Peng, Xian; Yang, Chongyang] Cent China Normal Univ, Natl Engn Res Ctr E Learning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Peng, Xian] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
关键词：
behavioral and sentimental analytics;topic modeling;learning analytics;behavior-sentiment topic mixture
摘要：
Course reviews, which is designed as an interactive feedback channel in Massive Open Online Courses, has promoted the generation of large-scale text comments. These data, which contain not only learners' concerns, opinions and feelings toward courses, instructors, and platforms but also learners' interactions (e.g., post, reply), are generally subjective and extremely valuable for online instruction. The purpose of this study is to automatically reveal these potential information from 50 online courses by an improved unified topic model Behavior-Sentiment Topic Mixture, which is validated and effective for detecting frequent topics learners discuss most, topics-oriented sentimental tendency as well as how learners interact with these topics. The results show that learners focus more on the topics about course-related content with positive sentiment, as well as the topics about course logistics and video production with negative sentiment. Moreover, the distributions of behaviors associated with these topics have some differences.
期刊：
International Journal of Distance Education Technologies,2017年15(3):1-14 ISSN：1539-3100
通讯作者：
Liu, SY
作者机构：
[Liu, Sanya; Ni, Cheng; Liu, Zhi; Cheng, Hercy N.H.; Peng, Xian] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
通讯机构：
[Liu, SY ] ;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
关键词：
Sanya Liu;Cheng Ni;Zhi Liu;Xian Peng;Hercy N.H. Cheng;Mining Individual Learning Topics in Course Reviews Based on Author Topic Model: Education;IGI Global;Journal Article;Education;IGI Publishing
摘要：
Nowadays, Massive Open Online Courses (MOOC) has obtained a rapid development and drawn much attention from the areas of learning analytics and artificial intelligence. There are lots of unstructured data being generated in online reviews area. The learning behavioral data become more and more diverse, and they prompt the emergence of big data in education. To mine useful information from these data, we need to use educational data mining and learning analysis technique to study the learning feelings and discussed topics among learners. This paper aims to mine and analyze topic information hidden in the unstructured reviews data in MOOC, a novel author topic model based on an unsupervised learning idea is proposed to extract learning topics for the each learner. According to the experimental results, we will analyze and focuses of interests of learners, which facilitates further personalized course recommendation and improve the quality of online courses.
摘要：
Currently, with the increasing advancement of interactive learning technologies in MOOCs, a large number of student-generated comments (SGCs) have been substantially produced with two primary emotions (positive and negative). The emotional orientations are typically related with specific learning topics or aspects discussed, which is of value to offer abundant academic feedbacks for teachers and developers. Especially, the negative emotion and topics can be exploited to get an in-depth insight of the problems and barriers encountered by learners in online learning. However, it is challenging to capture relevant details from unstructured SGCs. In this paper, we propose a generative probabilistic model that extends Sentence-LDA (SLDA), namely Emotion Topic Joint Probabilistic Model (ETJM), to explore negative opinions in terms of pairs of <emotion, topic> which we call emo-topic. The model first automatically extracts the sentences with the high negative emotion density (NED), and then incorporates emotion and topic together to explore negative emotional feedbacks towards topics. The experimental results show that learners extended some negative comments towards the issues about learning content, online assignments and certificates of courses. The summarization of these issues can be given back to teachers to regulate and improve the teaching methods, strategies and design of learning contents.
作者：
Sanya Liu;Zhenfan Hu;Xian Peng;Zhi Liu;Hercy N. H. Cheng;...
期刊：
International Journal of Distance Education Technologies,2017年15(1):15-27 ISSN：1539-3100
作者机构：
[Jianwen Sun; Hercy N. H. Cheng; Zhenfan Hu; Sanya Liu; Zhi Liu; Xian Peng] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
Sanya Liu;Zhenfan Hu;Xian Peng;Zhi Liu;H. N. H. Cheng;Jianwen Sun;Mining Learning Behavioral Patterns of Students by Sequence Analysis in Cloud Classroom: Education;IGI Global;Journal Article;Education;IGI Publishing
期刊：
International Journal of Innovative Computing, Information and Control,2016年12(6):2099-2110 ISSN：1349-4198
通讯作者：
Liu, Sanya(lsy5918@gmail.com)
作者机构：
[Sun, Jianwen; Liu, Sanya; Liu, Zhi; Peng, Xian; Gan, Wenbin] National Engineering Research Center for E-Learning, Central China Normal University, No. 152, Luoyu Road, Wuhan, 430079, China
通讯机构：
National Engineering Research Center for E-Learning, Central China Normal University, No. 152, Luoyu Road, Wuhan, China
作者：
Liu, Zhi*;Zhang, Wenjing;Sun, Jianwen（孙建文）;Cheng, Hercy N. H.;Peng, Xian;...
作者机构：
[Sun, Jianwen; Liu, Zhi; Cheng, Hercy N. H.; Peng, Xian; Liu, Sanya; Zhang, Wenjing] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
会议名称：
5th International Conference on Educational Innovation through Technology (EITT)
会议时间：
SEP 22-24, 2016
会议地点：
Tainan, TAIWAN
会议主办单位：
[Liu, Zhi;Zhang, Wenjing;Sun, Jianwen;Cheng, Hercy N. H.;Peng, Xian;Liu, Sanya] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
关键词：
Massive Open Online Course (MOOC);course comments;emotion recognition;topic mining;learning analytics
摘要：
Massive Open Online Course (MOOC) has been drawn much attention from learners and teachers through the world. MOOC offers a variety of interactive ways, in which the course comment panel is used for express students' opinions and feelings. These comments generally contain some learning problems, attitudes towards the course or the platform support, etc. The feedback information is beneficial for the exchange of ideas among teachers, learners and educational administrators. However, it is quite time-consuming to analyze these important opinions entirely by artificial reading. It is imperative that the MOOC needs the machine learning methods to detect the emotions and topics in text data. In this paper, we propose an application framework and design scheme of intelligent system for the emotion recognition and topic mining, aiming at conducting the intelligent and personalized learning analytics on MOOC. The purposes of the intelligent comment mining system include (1) predicting popularity level of each course; (2) obtaining emotion-topic feedbacks about content of courses for teachers to analyze and improve their teaching strategies; (3) obtaining emotion-topic feedbacks about platform support for administrators to improve user experiences in platform.
摘要：
Sentiment recognition of online course reviews is valuable to understand emotions and feelings of learners. Nowadays, an increasing number of course reviews are being generated with the emergence of Massive Open Online Courses (MOOCs), which offers teachers a chance to analyze the opinions of learners and improve teaching strategies. However, the unstructured data contain large amounts of redundant features, which will significantly impact the performance of machine learning. To select effective emotional features, we adopt a multi-swarm particle swarm optimization (MSPSO) method, which generates multi diverse particle swarms on several cross training subsets. These swarms are utilized to find the best features by the F-Measure fitness function. The experimental results on the real-life dataset show that MSPSO can effectively reduce redundancy of text features and capture discriminative features. Compared with conventional feature selection methods, MSPSO can gain the better performance when selecting the same dimensions. Besides, the result of a user survey indicates that 72.19% of subjects approve of the usability of the recognition results and effectiveness of the feature selection. MSPSO is effective to pick discriminative features in course reviews.Discriminability of features is used to iteratively optimize particle swarms.MSPSO exploits the differences among sample subsets to form diverse swarms.
期刊：
International Journal of Computers and Applications,2015年37(3-4):94-101 ISSN：1206-212X
通讯作者：
Liu, Sanya(lsy5918@gmail.com)
作者机构：
[Jianwen Sun; Zhi Liu; Sanya Liu; Lin Liu; Meng Wang; Xian Peng] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, P.R. China
通讯机构：
[Sanya Liu] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, P.R. China
作者机构：
[Wu, Di; Peng, Xian] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
会议名称：
International Conference on Intelligent Environments (IE)
会议时间：
JUN 30-JUL 04, 2014
会议地点：
Shanghai, PEOPLES R CHINA
会议主办单位：
[Wu, Di;Peng, Xian] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
关键词：
cloud computing;E-Learning standard;cloud-based education standard
摘要：
Cloud computing technology provides a convenient way for the distribution and application of educational resource, which becomes the hotspot of ICT in education. Based on in-depth study of standards in cloud computing and E-Learning, the paper analyzes the requirement, key techniques and actual application of cloud computing education, constructs a standard architecture, elaborates the relationship between different standards in this architecture.
